The Target Detection of Wear Particles in Ferrographic Images Based on the Improved YOLOv8

An enhanced YOLOv8 algorithm is proposed in the following paper to address challenging issues encountered in ferrographic image target detection, such as the identification of complex-shaped wear particles, overlapping and intersecting wear particles, and small and edge-wear particles. This aim is achieved by integrating the main body network with the improved Deformable Convolutional Network v3 to enhance feature extraction capabilities. Additionally, the Dysample method is employed to optimize the upsampling technique in the neck network, resulting in a clearer fused feature image and improved precision for detecting small and edge-wear particles. In the head network, parameter sharing simplifies the detection head while enhancing convergence speed and precision through improvements made to the loss function. The experimental results of the present study demonstrate that compared to the original algorithm, this enhanced approach achieves an average precision improvement of 5.6% without compromising the detection speed (111.6FPS), therefore providing valuable support for online monitoring device software foundations.